Risk Factors Associated with Urban Youth Unemployment in Oromia, Ethiopia: A Cross-Sectional Study of Labor Market Challenges Using Logistic Regression

Objective. This study conducted in Nekemte City, Eastern Wollega, Oromia, Ethiopia where over two million youth enroll the labour market in cities annually, and lack of employment is still a serious problem. This study aims at assessing the extent and underlying risks of urban unemployment among young people in the study setting. Methods: The study employed a cross-sectional household-based design and a random sample of 463 youth was recruited using a multi-stage sampling process in four purposively selected sub-cities in Nekemte. Standard household survey questionnaires were adopted and A pilot study was conducted on 10% of the overall sample, selected from a neighboring sub-city, to validate the data collection tools and procedure. A group of eight enumerators and four supervisors was recruited and well trained to reassure quality of data collection. The data was entered and cleaned up using EpiData 3.1 and finally analyzed using SPSS version 24.0. The Variance Inflation Factor (VIF) > 10 indicates redundancy among explanatory variables, and a binary logistic regression model was adjusted to identify determinant factors influencing urban youth unemployment in the study area. Results: The descriptive results show a response rate of 94.0%, 68% prevalence of unemployment with over two-thirds (67.5%) of rural out-migrants citing insecurity as the primary driver. Furthermore, 78% demonstrated lack of access to credit, 60% cited poor employment-seeking habits, 67.3% reported psychological strain, and 59.5% experienced socio-economic hardships of reduced living standards. The adjusted odds ratios show clear association of unemployment with gender: female youth were 4.13 times more likely to be unemployed than males [AOR = 4.13; 95% CI: 2.2–7.7, p < 0.05]. The findings also show that youths aged 19 years or younger were 2.19 times more likely to be unemployed compared to their counterparts who aged 20 or older [AOR = 2.19; 95% CI: 1.2–3.9; p < 0.05], youths who lacked access to credit were 5.34 times more likelihoods of being unemployed [AOR = 5.34; 95% CI: 2.8–9.8; p < 0.05] than their counterparts. Socio-economic and demographic factors, particularly insecurity-induced youth migrants and youths without entrepreneurial competencies, experienced 1.86 and 5.49 times higher risk of unemployment [AOR = 5.49; 95% CI: 1.5–9.7; p = 0.05] compared to their counterparts, respectively. Job creation deficits by local municipal authorities and the mismatch between acquired skills and labor market demands among industries increased risks of urban youth unemployment by 2.08 and 3.22 times higher than respective counterparts, respectively. Conclusion: proposed program interventions include considering females, youth aged 19 years or younger, making ease access to financial credits, aligning acquired skills to labor market demand, improving business skills, offering psychological support services, and promoting job creation strategies by the municipal. Realizing these predictors was essential to address the demographic, structural, socio-economic and psychological factors associated with the increasing unemployment risks in the study setting.